Instructions to use jackcloudman/Leanstral-2603-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use jackcloudman/Leanstral-2603-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="jackcloudman/Leanstral-2603-GGUF", filename="mistralai_Leanstral-128x3.9B-2603-Q4_K_M.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use jackcloudman/Leanstral-2603-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf jackcloudman/Leanstral-2603-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf jackcloudman/Leanstral-2603-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf jackcloudman/Leanstral-2603-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf jackcloudman/Leanstral-2603-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf jackcloudman/Leanstral-2603-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf jackcloudman/Leanstral-2603-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf jackcloudman/Leanstral-2603-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf jackcloudman/Leanstral-2603-GGUF:Q4_K_M
Use Docker
docker model run hf.co/jackcloudman/Leanstral-2603-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use jackcloudman/Leanstral-2603-GGUF with Ollama:
ollama run hf.co/jackcloudman/Leanstral-2603-GGUF:Q4_K_M
- Unsloth Studio new
How to use jackcloudman/Leanstral-2603-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for jackcloudman/Leanstral-2603-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for jackcloudman/Leanstral-2603-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for jackcloudman/Leanstral-2603-GGUF to start chatting
- Docker Model Runner
How to use jackcloudman/Leanstral-2603-GGUF with Docker Model Runner:
docker model run hf.co/jackcloudman/Leanstral-2603-GGUF:Q4_K_M
- Lemonade
How to use jackcloudman/Leanstral-2603-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull jackcloudman/Leanstral-2603-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Leanstral-2603-GGUF-Q4_K_M
List all available models
lemonade list
I-Quants IQ6 or Unsloth UD 6Bit? 😎
🤗 1
#3 opened about 2 months ago
by
wa999
Q8 working successfully on H100
🤗 1
#2 opened about 2 months ago
by
coughdrop